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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "图像分类数据集中最常用的是手写数字识别数据集MNIST。但大部分模型在MNIST上的分类精度都超过了95%。为了更直观地观察算法之间的差异，使用一个图像内容更加复杂的数据集Fashion-MNIST（这个数据集也比较小，只有几十M，没有GPU的电脑也能吃得消）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# torchvision包是服务于PyTorch深度学习框架的，主要用来构建计算机视觉模型\n",
    "# torchvision.datasets: 一些加载数据的函数及常用的数据集接口；\n",
    "# torchvision.models: 包含常用的模型结构（含预训练模型），例如AlexNet、VGG、ResNet等；\n",
    "# torchvision.transforms: 常用的图片变换，例如裁剪、旋转等；\n",
    "# torchvision.utils: 其他的一些有用的方法。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "import sys"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过torchvision的torchvision.datasets来下载数据集,通过参数train来指定获取训练数据集或测试数据集,指定了参数transform = transforms.ToTensor()使所有数据转换为Tensor,如果不进行转换则返回的是PIL图片。transforms.ToTensor()将尺寸为 (H x W x C) 且数据位于[0, 255]的PIL图片或者数据类型为np.uint8的NumPy数组转换为尺寸为(C x H x W)且数据类型为torch.float32且位于[0.0, 1.0]的Tensor\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于像素值为0到255的整数，所以刚好是uint8所能表示的范围，包括transforms.ToTensor()在内的一些关于图片的函数就默认输入的是uint8型，若不是，可能不会报错但可能得不到想要的结果。所以，如果用像素值(0-255整数)表示图片数据，那么一律将其类型设置成uint8，避免不必要的bug。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 获取数据集\n",
    "# 训练集中和测试集中的每个类别的图像数分别为6,000和1,000。\n",
    "# 因为有10个类别，所以训练集和测试集的样本数分别为60,000和10,000\n",
    "# ashion-MNIST是一个10类服饰分类数据集\n",
    "\n",
    "mnist_train = torchvision.datasets.FashionMNIST(root=\"Datasets/FashionMNIST\", train=True, download=True, transform=transforms.ToTensor())\n",
    "mnist_test = torchvision.datasets.FashionMNIST(root=\"Datasets/FashionMNIST\", train=False, download=True, transform=transforms.ToTensor())\n",
    "# mnist_train和mnist_test都是torch.utils.data.Dataset的子类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'torchvision.datasets.mnist.FashionMNIST'> 60000\n",
      "<class 'torchvision.datasets.mnist.FashionMNIST'> 10000\n"
     ]
    }
   ],
   "source": [
    "print(type(mnist_train), len(mnist_train))\n",
    "print(type(mnist_test), len(mnist_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 28, 28]) 9\n"
     ]
    }
   ],
   "source": [
    "# 可以通过下标来访问任意一个样本\n",
    "feature, label = mnist_train[0]\n",
    "print(feature.shape, label) # Channel x Height x Width"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# t-shirt（T恤）、trouser（裤子）、pullover（套衫）、dress（连衣裙）、coat（外套）、sandal（凉鞋）、shirt（衬衫）、sneaker（运动鞋）、bag（包）和ankle boot（短靴）\n",
    "\n",
    "def get_fashion_mnist_labels(labels):\n",
    "    text_labels = ['t-shirt', 'trouser', 'pullover', 'dress', 'coat',\n",
    "                   'sandal', 'shirt', 'sneaker', 'bag', 'ankle boot']\n",
    "    return [text_labels[int(each)] for each in labels]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 在一行里画出多张图像和对应标签的函数\n",
    "\n",
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "from IPython import display\n",
    "\n",
    "def use_svg_display():\n",
    "    # 用矢量图显示\n",
    "    display.set_matplotlib_formats('svg')\n",
    "\n",
    "def set_figsize(figsize=(3.5, 2.5)):\n",
    "    use_svg_display()\n",
    "    # 设置图的尺寸\n",
    "    plt.rcParams['figure.figsize'] = figsize\n",
    "    \n",
    "def show_fashion_mnist(images, labels):\n",
    "    use_svg_display()\n",
    "    _, figs = plt.subplots(1, len(images), figsize=(10,10))\n",
    "    for f, img, label in zip(figs, images, labels):\n",
    "        f.imshow(img.view(28,28).numpy())\n",
    "        f.set_title(label, fontsize=8, color='b')\n",
    "        f.axes.get_xaxis().set_visible(False)\n",
    "        f.axes.get_yaxis().set_visible(False)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "<Figure size 720x720 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 看一下训练数据集中前10个样本的图像内容和文本标签\n",
    "X, y = [], []\n",
    "for i in range(10):\n",
    "    X.append(mnist_train[i][0])\n",
    "    y.append(mnist_train[i][1])\n",
    "show_fashion_mnist(X, get_fashion_mnist_labels(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 读取小批量数据\n",
    "# mnist_train是torch.utils.data.Dataset的子类\n",
    "# 可以将其传入torch.utils.data.DataLoader来创建一个读取小批量数据样本的DataLoader实例\n",
    "# 允许使用多进程来加速数据读取，用num_workers来设置4个进程读取数据\n",
    "\n",
    "batch_size = 256\n",
    "if sys.platform.startswith('win'):\n",
    "    num_workers = 0  # 0表示不用额外的进程来加速读取数据\n",
    "else:\n",
    "    num_workers = 4\n",
    "    \n",
    "train_iter = torch.utils.data.DataLoader(mnist_train, batch_size=batch_size, shuffle=True, num_workers=num_workers)\n",
    "test_iter = torch.utils.data.DataLoader(mnist_test, batch_size=batch_size, shuffle=False, num_workers=num_workers)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.49 sec\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "for X, y in train_iter:\n",
    "    continue\n",
    "print('%.2f sec' % (time.time() - start))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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